# Mossad: Defeating Software Plagiarism Detection

arXiv: https://arxiv.org/abs/2010.01700 · HTML: https://mlsanity.com/paper/2010.01700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Fooling MOSS Detection with Pretrained Language Models](https://mlsanity.com/paper/2201.07406/) (2022)
- [Can ChatGPT Pass An Introductory Level Functional Language Programming Course?](https://mlsanity.com/paper/2305.02230/) (2023)
- [LLMPirate: LLMs for Black-box Hardware IP Piracy](https://mlsanity.com/paper/2411.16111/) (2024)
- [Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection](https://mlsanity.com/paper/2206.08726/) (2022)
- [Adversarial Attacks against Binary Similarity Systems](https://mlsanity.com/paper/2303.11143/) (2023)

## Abstract (arXiv)

Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work. This paper shows this is not the case. It presents an entirely automatic program transformation approach, Mossad, that defeats popular software plagiarism detection tools. Mossad comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. Mossad is effective at defeating four plagiarism detectors, including Moss and JPlag. Mossad is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its non-deterministic approach, Mossad can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of Mossad across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate Mossad-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education.